基于分水岭修正与U-Net的肝脏图像分割算法  

Liver Image Segmentation Algorithm Based on Watershed Correction and U-Net

作　　者：亢洁[1] 丁菊敏 万永[3] 雷涛[2] KANG Jie;DING Jumin;WAN Yong;LEI Tao(School of Electrical and Control Engineering,Shaanxi University of Science and Technology,Xi’an 710021,China;School of Electronic Information and Artificial Intelligence,Shaanxi University of Science and Technology,Xi’an 710021,China;Department of Geratic Surgery,The First Affiliated Hospital of Xi’an Jiaotong University,Xi’an 710061,China)

机构地区：[1]陕西科技大学电气与控制工程学院,西安710021 [2]陕西科技大学电子信息与人工智能学院,西安710021 [3]西安交通大学第一附属医院老年外科,西安710061

出　　处：《计算机工程》2020年第1期255-261,270,共8页Computer Engineering

基　　金：国家自然科学基金(61871259,61811530325)

摘　　要：在利用卷积神经网络分割肝脏边界较模糊的影像数据时容易丢失位置信息,导致分割精度较低。针对该问题,提出一种基于分水岭修正与U-Net模型相结合的肝脏图像自动分割算法。利用U-Net分层学习图像特征的优势,将浅层特征与深层语义特征相融合,避免丢失目标位置等细节信息,得到肝脏初始分割结果。在此基础上,通过分水岭算法形成的区域块对肝脏初始分割结果的边界进行修正,以获得边界平滑精确的分割结果。实验结果表明,与传统的图割算法和全卷积神经网络算法相比,该算法能够实现更为精准的肝脏图像分割。When Convolutional Neural Network(CNN)is used for liver image segmentation with blurred boundaries,the segmentation precision is reduced due to the frequent loss of location information.To address the problem,this paper proposes an automated liver image segmentation algorithm that combines the watershed correction and the U-Net model.The algorithm takes advantages of U-Net in layered learning of image features,so as to achieve fusion of shallow features and deep features without loss of detailed information,such as the location of the target.After the initial result of liver image segmentation is obtained,the boundaries of the initial result is corrected by using blocks formed by the watershed algorithm,so as to obtain a segmentation result with smooth and precise boundaries.Experimental results show that the proposed algorithm can implement more precise liver image segmentation compared with the existing graph-cut algorithm and the Fully Convolutional Network(FCN)algorithm.

关 键 词：肝脏图像分割 卷积神经网络 U-Net模型 分水岭算法 边界修正 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]